Search NASA⌕ Search

SEARCH · Search NASA

Results for “on-road”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

High-Mileage Courier Fleet Vehicle On-Road Logger Data

This dataset describes the performance and fuel efficiency of AVTA test vehicles operating in commercial courier fleets the Phoenix, AZ metro area between 2010 and 2016. Aftermarket data loggers were installed in two to four vehicles of each of 30+ distinct year/make/models (see reference ["INL Advanced Vehicle Testing Activity: On-road Logger and Laboratory Battery Pack Testing Vehicle List"](https://avt.inl.gov/sites/default/files/pdf/reports/DatasetVehicleList.pdf) for full list of vehicles). Loggers recorded vehicle operation as they were driven up to 160,000 miles in up to three years of fleet testing. Parameters were logged at 1-second intervals, including - vehicle speed, - engine and/or electric motor speed, - fuel and/or electricity consumption, and - ambient temperature. This dataset includes both raw second-by-second data and trip-level metrics. This dataset is shared by API; a small sample of the vehicle and logger data has been extracted and is also available for download.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Audi e-tron Green Light Optimized Speed Advisory On-Road Data

To aid researchers in studying the capabilities and benefits of vehicle-to-infrastructure communication, Argonne National Laboratory collected a robust set of on-road driving data of the Audi Green Light Optimized Speed Advisory (GLOSA) system implemented in the e-tron battery electric vehicle. This dataset includes 33 tests, each roughly 27 miles in length and roughly 45 to 75 minutes in duration. The team selected Kane County Highway Route 34 from Main Street in Batavia, Illinois to Middlecreek Lane in St. Charles, Illinois as the route do to its high density of GLOSA-active lights and the most opportunities to observe the system per hour of test time. The data include parameters from the following sources: GLOSA system driving the dash indicators, multiple powertrain parameters including real-time battery power/energy consumption, GPS, front radar gap, and rear radar gap. ![audio-e-tron image](audi-e-tron.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Cadillac CT6 Super Cruise On-road Data

This dataset encompasses about 60 individual drives of a 2019 Cadillac CT6 with Super Cruise in its relevant operational domain covering more than 1,000 miles. Adhering to the limited operational design domain of the investigated version of the Supercruise system, the majority of the data was collected during highway driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. ![cadillac-ct6 image](ct6.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Tesla Model 3 Autopilot On-road Data

This dataset encompasses about 60 individual drives of a 2020 Tesla Model 3 with Autopilot in its relevant operational domain covering more than 1,000 miles. The majority of the data was collected during highway and suburban driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. ![tesla m3 image](tesla-m3.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) - Predictive Data-Driven Vehicle Dynamics and Powertrain Control: from ECU to the Cloud (Final Scientific/Technical Report)

This project developed and demonstrated a predictive, data-driven vehicle control system designed to improve energy efficiency and driving performance. The team created intelligent self-driving car technology that optimizes fuel and electricity use by proactively planning vehicle actions. By combining Level 4 autonomous driving capabilities with vehicle-to-everything (V2X) connectivity, the system enables vehicles to adjust speed and change lanes in response to traffic signals, surrounding vehicles, and road conditions, reducing unnecessary stops and delays. In testing, the system improved vehicle fuel economy by more than 30% and reduced travel time by approximately 10%, compared to a conventional adaptive cruise control baseline. These results demonstrate the technical effectiveness of using predictive, V2X-enabled strategies, such as traffic light timing and surrounding traffic awareness, to inform real-time vehicle powertrain control and driving behavior. Additionally, a supporting cloud platform was developed to provide dispatch and route recommendations as well as to log vehicle data, demonstrating the economic feasibility of this approach at the fleet level. By optimizing dispatching and routing operations, this technology enables electric fleet operators to use their vehicles more efficiently and reduce reliance on diesel backups, lowering both operating costs and energy consumption. Overall, this project’s technology advances the future of clean, energy-efficient transportation, enabling vehicles and fleets to reduce energy waste, cut costs, and lower emissions through intelligent automation and connectivity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

NEXT Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR Phase I & II)

The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Trends and 2025 Insights on the Rise of Electric Vehicles in the USA

Plug-in electric vehicles (EVs) are reshaping the transportation energy landscape, providing a practical alternative to petroleum fuels for a growing number of applications. EV sales grew 55x in the past decade (2014-2024) and 6x since 2020, driven by technological progress enabled by policies to reduce transportation emissions as well as industrial plans motivated by strategic value of EVs for global competitiveness, jobs and geopolitics. In 2024, 22% of passenger cars sold globally were EVs and opportunities for EVs beyond on-road applications are growing, including solutions to electrify off-road vehicles, maritime and aviation. This Review updates and expands our 2020 assessment of the scientific literature and describes the current status and future projections of EV markets, charging infrastructures, vehicle-grid integration and supply chains in the USA. EV is the lowest-emission motorized on-road transportation option, with life-cycle emissions decreasing as electricity emissions continue to decrease. Charging infrastructure grew in line with EV adoption but providing ubiquitous reliable and convenient charging remains a challenge. EVs are reducing electricity costs in several US markets and coordinated EV charging can improve grid resilience and reduce electricity costs for all consumers. The current trajectory of technology improvement and industrial investments points to continued acceleration of EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Transportation in net-zero emissions futures: Insights from the EMF-37 model intercomparison study

Transportation is currently the largest source of U.S. anthropogenic CO 2 emissions, at about a third of the total. Achieving net-zero emissions by mid-century will require substantial reductions in transportation emissions across passenger and freight travel. Here we leverage a model intercomparison study to explore the role of transportation in scenarios achieving net-zero economy-wide CO 2 emissions by 2050. We find the transport sector is poised to play the most significant role in reducing demand-side emissions, mostly driven by technology substitution, as modeling results suggest a limited role for mode shifting and for reduced use of personal car travel in the U.S. Among various technology solutions, models show agreement that passenger on-road vehicles will largely transition to electric vehicles (EVs), while solutions to decarbonize heavier travel modes are more diverse and include greater use of liquid biofuels and hydrogen. Research should continue to investigate the evolution of on-road electrification, the role of biofuels and hydrogen across heavier travel modes, and the role of mode shifting and travel behavior change to support personal transportation decarbonization at national and regional scales to temper the rapid growth in clean fuel and electricity demand.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Impact of Cyber Threat Awareness on Driver Response to an Unexpected Vehicle Cyberattack

Here, the integration of advanced cyber-physical systems in heavy vehicles introduces new vulnerabilities by expanding the possibility of cyberattacks. The objective of this study is to evaluate (1) how threat awareness influences driver response to an unexpected cyberattack, (2) how the provision of a basic cyberattack response protocol influences driver performance, and (3) how professionally trained versus standard drivers compare in their responses to a cyberattack. An on-road driving study (N = 50) was conducted using a medium heavy-duty vehicle. Participants were divided into three groups: Control, which remained unaware of any potential cyberattack; Aware, which was informed about the potential cyberattack; and Aware + Protocol, which received the same warning as the Aware group with the addition of a basic cyberattack response protocol. An instrument cluster cyberattack was executed at the same location for all participants. The findings highlight the essential role of awareness and response protocol in enhancing driver response to an unexpected vehicle cyberattack. The Aware + Protocol group had the highest stop rate (100%) and the shortest stopping distances (224 m for standard drivers and 254 m for professionals), compared to the Control group (828 and 520 m, respectively). Aware + Protocol also had the fastest reaction time, averaging 7.53 s, versus 16.12 s (Aware) and 30.29 s (Control). These results emphasize that awareness alone is insufficient. Providing drivers with clear, actionable protocols significantly improves their ability to react quickly and safely to cyberattacks, enhancing overall road safety.

Cybersecurity↗
DOE OSTI2025

Impact of truck electrification on air pollution disparities in the United States

Electrifying heavy-duty trucks reduces on-road diesel emissions but shifts the burden of supplying energy to power-generation facilities. The combined effect of Inflation Reduction Act investments in grid decarbonization and truck electrification will alter the magnitude and distribution of air pollution burdens across the United States. These investments are intended to facilitate a just energy transition, with 40% of the benefits flowing to disadvantaged communities per the Justice40 Initiative. Here we evaluate the combined effects of Inflation Reduction Act grid decarbonization and truck electrification investments on a national scale to determine whether the air pollution benefits would meet this 40% goal for both disadvantaged communities and the most exposed racial–ethnic groups. We find that truck electrification and decarbonization reduce air-pollution-related premature mortality in disadvantaged communities. However, the relative disparity between disadvantaged and non-disadvantaged communities increases, suggesting that a disproportionate share of benefits accrue to non-disadvantaged communities. Whereas absolute disparity in grid emissions decreases over time for all racial–ethnic groups, relative disparity remains largely unchanged, with Black populations being the most exposed. Electrifying drayage corridors would result in comparatively large health benefits for disadvantaged communities, suggesting that increasing targeted electrification investments in short-haul routes near urban areas (for example, ports) could be promising.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Speed Variation Based Power Regulation Concept for Dynamic Wireless Charging

On-road wireless charging of electric vehicles (EVs) in-motion could potentially reduce range anxiety or battery size with wide-spread deployment. The planning and implementation of such systems are greatly complicated due to their susceptibility to load variation inherent to traffic flow. Here, this paper proposes a method for derisking the potential for traffic slowdowns by compensating for reduced vehicle speed and investigates how implementation may affect system performance. A load modeling case study is presented at 200kW for a mile of high-speed roadway employing speed-based power regulation with results indicating average power usage and maximum car hosting capability can be reduced by 20% and increased by 30% respectively. An 85kHz power electronics model is developed based on designs and prototypes for an 11kW, 190m airgap static system and a 200kW dynamic wireless track. The simulation is validated in the 11kW experimental prototype and modified for 200kW operation to compare with simulated performance. Sensitivity studies are performed in MATLAB/Simulink to evaluate how parameters influence system performance and confirm the capability to reduce output power and maintain efficiency at 11 and 200kW. The static 11kW experimental system operates at 93.6% efficiency and multiple options exist to reduce power while maintaining efficiency greater than 90%. The capability to dynamically modify power output from WPT coils, in an experimentally validated simulation, enables techniques to significantly mitigate load variability due to reductions in vehicle speed.

42 ENGINEERING↗
DOE OSTI2025

Simultaneous Control of Unburned NH 3 and NO x Emissions From High Load Dual-Fuel Ammonia Operation on a High-Speed Diesel Engine Using a Cu-SCR System

Dual-fuel ammonia strategies are being investigated as a promising way to utilize NH 3 as an alternative fuel for internal combustion engines in the maritime sector. One of the remaining barriers to implementing dual-fuel NH 3 combustion strategies is understanding ways to minimize unburned NH 3 and nitrogen oxide (NO x ) emissions from these engines, both of which are elevated relative to a conventional diesel baseline. Selective catalytic reduction (SCR) systems are widely used for lean NO x emission controls for engines across transportation and stationary energy applications. SCR systems use a reducing agent, such as NH 3 , to react with NO x in the exhaust, converting it into nitrogen and water. Typically, NH 3 is injected into the exhaust as a urea solution. In dual-fuel NH 3 engines, where unburned NH 3 is present in the exhaust, an SCR system could be used to mitigate both NH 3 and NO x emissions. The presented work evaluates a commercial copper-zeolite SCR and ammonia slip catalyst system, designed for on-road diesel engine applications, for controlling unburned NH 3 and NO x emissions from a dual-fuel NH 3 combustion engine. The aftertreatment system was installed downstream of a single-cylinder four-stroke diesel engine that has been modified for dual-fuel ammonia use. Furthermore, the emissions were characterized by using a Fourier transform infrared spectrometer for both late- and early-injection diesel pilot strategies over three air–fuel equivalence ratios spanning from 1.6 to 1.0 at 1200 rpm and 12.6 bar IMEP g condition (with greater than 95% ammonia energy fraction). Initial findings indicate that the SCR achieves more than 99% NO x conversion with less than 50 ppm NH 3 slip at air–fuel equivalence ratios greater than 1.4 at the operating conditions investigated. However, these benefits are accompanied by additional N 2 O emissions that are formed over the Cu-SCR.

Catalysts↗
DOE OSTI2026

High-Mileage Courier Fleet Vehicle Laboratory Battery Pack Testing

For one of each of the AVTA's plug-in hybrid electric vehicles, battery electric vehicles, and some hybrid electric vehicles tested in high-mileage courier fleets, the high-voltage traction battery packs were removed from the vehicle and tested at the beginning and end of fleet testing. For some vehicles, batteries also were tested at periodic intervals during fleet testing. Standard reference performance tests were conducted to characterize battery degradation over time. This dataset contains results from two or more rounds of battery tests for 21 distinct year/make/model vehicles (see reference ["INL Advanced Vehicle Testing Activity: On-road Logger and Laboratory Battery Pack Testing Vehicle List"](https://avt.inl.gov/sites/default/files/pdf/reports/DatasetVehicleList.pdf) for full list of vehicles). Each round of battery testing included the "Static Capacity Test" and the "Hybrid Pulse Power Characterization (HPPC) Test", conducted according to test procedures published in the United States Advanced Battery Consortium ["Battery Test Manual For Power-Assist Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=57), ["Battery Test Manual For Plug-In Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=168), and ["Electric Vehicle Battery Test Procedures Manual"](https://www.uscar.org/commands/files_download.php?files_id=5) prior to the time of testing. These tests were performed by Intertek Testing Services, North America. This dataset is shared by API; a small sample of the vehicle battery and test data has been extracted and is also available for download.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Propulsion System Hybridization and Electrification and Energy Recovery of the Hydraulic System for a Diesel-Powered Off-Road Carry/Lift Machine Final Technical Report (FTR)

Historically, heavy-duty off-road equipment has prioritized performance, reliability, and cost, with limited emphasis on efficiency. While increasing carbon dioxide (CO 2 ) regulations have driven a focused effort for efficiency in on-road vehicles, off-road machines have remained largely overlooked, leaving significant opportunities for efficiency improvements and emissions reductions. To address this overlooked industry, Michigan Technological University’s Advanced Power Systems Research Center (APS LABS) worked with Pettibone to determine a unique electrified architecture for Pettibone’s heavy-duty off-road material handler, the Cary-Lift 204i, with a goal of 20% fuel savings. The base Cary-Lift uses a diesel engine with no electrification.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

EVs@Scale FUSE Project Review

This project will analyze and demonstrate SCM and VGI approaches to reduce grid impacts from EVs@Scale as a result of the charging needs of the LD, MD, and HD on-road electrified fleet.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

A Forward-Looking Dataset of EV Managed Charging Resource and Costs

This presentation summarizes a high-resolution, forward-looking dataset of EV adoption, EV charging, and managed charging resource. Vehicle-level data are grounded in current adoption and charging patterns, and ~200,000 real-world vehicle-weeks of travel data covering all on-road segments (i.e., light-duty, transit and school buses, local, regional and long-haul medium- and heavy-duty). The data, which include multiple charging profiles per vehicle to bound flexibility, are then processed and aggregated to describe baseline charging and charge management resource by county, hour, year, scenario, and vehicle type. Coupled with one of four scenarios of how EV managed charging costs might evolve over time, the dataset enables a power sector capacity expansion model to select cost-optimal quantities of EV managed charging and supply-side resources to reliably satisfy demand. Five integration strategies: Baseline, Daytime and Flat (passive), Flex (active), and Stress (anti-strategy), illustrate how baseline charging and flexibility potential changes with EVSE build-out and charging preferences.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

ELECTRIFICATION OF A HEAVY-DUTY OFF-ROAD MATERIAL HANDLER: ENERGY SAVINGS AND EMISSION REDUCTIONS

Federal regulations are driving the adoption of electrification technologies to reduce carbon dioxide equivalent (CO2e) emissions, a metric that quantifies the global warming potential of various greenhouse gases in terms of carbon dioxide (CO2). Although no specific CO2 regulations exist for heavy-duty off-road machines, future reductions are likely, given stricter emissions standards for on-road vehicles. The heavy-duty off-road sector offers significant fuel-saving potential, as its focus has traditionally been on reliability and performance rather than fuel efficiency. This dissertation examines fuel and CO2e savings opportunities on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i, from stock configuration to simple modifications to a complete teardown and reconfiguration of the machine with a plug-in series hybrid architecture using electrified hydraulics. The study begins by modeling the baseline machine’s fuel and energy consumption, calibrating with experimental data from custom operating cycles. An energy analysis identifies key areas for fuel savings. Two simple powertrain modifications result in a combined 16.2% fuel savings. Next, a Pugh-style analysis narrows a list of electrified architectures, leading to high-fidelity models that evaluate total lifetime CO2e and costs. Higher electrification levels reduce CO2e emissions but increase costs, and electricity grid emissions significantly impact CO2e for plug-in architectures. A plug-in series hybrid is chosen for the project. In its base control form, 49% fuel and 29% CO2e savings are expected from the plug-in series hybrid compared to the baseline machine. Further savings are pursued through regenerative braking (6.3%) and load-following hydraulic control (17.8%), totaling 24.1% fuel savings, and leading to a total of 61% fuel and 41% CO2e savings compared to the baseline. Battery chemistries and charging strategies are also analyzed for cost and CO2e impacts, finding LFP batteries as superior due to longevity, and overnight level 2 charging usually at a lower cost but resulting in higher emissions than opportunity DC fast-charging (DCFC). DCFC emissions are highly dependent on grid emissions, and DCFC cost is highly dependent on grid demand charges. Finally, artificial intelligence is applied to operating cycle recognition. Neural network accuracy ranges from 81% to 99%, with applications to worksite efficiency and safety improvements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Refining Control, Charging, and Battery Chemistry for CO 2 e Savings in Heavy-Duty Off-Road Plug-In Series Hybrid

With current and future regulations continuing to drive reductions in carbon dioxide equivalent (CO 2 e) emissions in the on-road industry, the off-road industry is also likely to be regulated for fuel and CO 2 e savings. This work focuses on converting a heavy-duty off-road material handler from a conventional diesel powertrain to a plug-in series hybrid, achieving a 49% fuel reduction and 29% CO 2 e reduction via simulation. Control strategies were refined for energy savings, including a regenerative braking strategy to increase regenerative braking and a load-following hydraulic strategy to decrease electrical energy consumption. The load-following hydraulic control shuts off the hydraulic electric machine when it is not needed—an approach not previously seen in a load-sensing, pressure-compensated system. Furthermore, these strategies achieved a 24.1% fuel savings, resulting in total savings of 61% in fuel and 41% in CO 2 e in the plug-in series compared to the conventional machine. Beyond control strategies, this study evaluated battery chemistry and charging strategy refinements for total cost of ownership (TCO) and lifetime CO 2 e. LFP batteries emerged as the most cost-effective and least emitting due to their longer lifespan, which reduced replacement frequency. Charging comparisons showed that Level 2 charging (L2C) typically resulted in lower TCO but higher lifetime CO 2 e than DC fast charging (DCFC). DCFC costs were heavily influenced by local demand charges, and DCFC emissions were heavily influenced by local grid emissions.

33 ADVANCED PROPULSION SYSTEMS↗